Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations67635
Missing cells380994
Missing cells (%)23.5%
Duplicate rows185
Duplicate rows (%)0.3%
Total size in memory12.4 MiB
Average record size in memory192.0 B

Variable types

Categorical2
Numeric15
Text3
Unsupported3
DateTime1

Alerts

Dataset has 185 (0.3%) duplicate rowsDuplicates
Bdrms is highly overall correlated with Fbath and 3 other fieldsHigh correlation
District is highly overall correlated with Taxkey and 1 other fieldsHigh correlation
Extwall is highly overall correlated with Fin_sqft and 1 other fieldsHigh correlation
Fbath is highly overall correlated with Bdrms and 1 other fieldsHigh correlation
Fin_sqft is highly overall correlated with Extwall and 1 other fieldsHigh correlation
Nbhd is highly overall correlated with Bdrms and 1 other fieldsHigh correlation
Nr_of_rms is highly overall correlated with ExtwallHigh correlation
PropType is highly overall correlated with NbhdHigh correlation
Rooms is highly overall correlated with Bdrms and 2 other fieldsHigh correlation
Stories is highly overall correlated with Fin_sqft and 1 other fieldsHigh correlation
Taxkey is highly overall correlated with DistrictHigh correlation
Units is highly overall correlated with Bdrms and 2 other fieldsHigh correlation
nbhd is highly overall correlated with taxkeyHigh correlation
taxkey is highly overall correlated with District and 1 other fieldsHigh correlation
PropType is highly imbalanced (60.1%) Imbalance
Taxkey has 37736 (55.8%) missing values Missing
CondoProject has 56939 (84.2%) missing values Missing
Nbhd has 37736 (55.8%) missing values Missing
Extwall has 13643 (20.2%) missing values Missing
Nr_of_rms has 37736 (55.8%) missing values Missing
Fin_sqft has 37736 (55.8%) missing values Missing
Bdrms has 1947 (2.9%) missing values Missing
Hbath has 4842 (7.2%) missing values Missing
PropertyID has 29901 (44.2%) missing values Missing
taxkey has 29901 (44.2%) missing values Missing
nbhd has 29958 (44.3%) missing values Missing
Rooms has 31847 (47.1%) missing values Missing
FinishedSqft has 30013 (44.4%) missing values Missing
Units is highly skewed (γ1 = 69.8035538) Skewed
Bdrms is highly skewed (γ1 = 241.7170329) Skewed
Lotsize is an unsupported type, check if it needs cleaning or further analysis Unsupported
Sale_price is an unsupported type, check if it needs cleaning or further analysis Unsupported
FinishedSqft is an unsupported type, check if it needs cleaning or further analysis Unsupported
Stories has 744 (1.1%) zeros Zeros
Nr_of_rms has 25173 (37.2%) zeros Zeros
Bdrms has 3794 (5.6%) zeros Zeros
Fbath has 5544 (8.2%) zeros Zeros
Hbath has 44282 (65.5%) zeros Zeros
Rooms has 935 (1.4%) zeros Zeros

Reproduction

Analysis started2025-08-12 17:54:55.041629
Analysis finished2025-08-12 17:55:06.496625
Duration11.45 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

PropType
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)< 0.1%
Missing13
Missing (%)< 0.1%
Memory size528.5 KiB
Residential
51686 
Condominium
9902 
Commercial
 
3632
Lg Apartment
 
2157
Vacant Land
 
205
Other values (2)
 
40

Length

Max length13
Median length11
Mean length10.978853
Min length6

Characters and Unicode

Total characters742412
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowResidential
2nd rowResidential
3rd rowResidential
4th rowResidential
5th rowResidential

Common Values

ValueCountFrequency (%)
Residential 51686
76.4%
Condominium 9902
 
14.6%
Commercial 3632
 
5.4%
Lg Apartment 2157
 
3.2%
Vacant Land 205
 
0.3%
Manufacturing 35
 
0.1%
Exempt 5
 
< 0.1%
(Missing) 13
 
< 0.1%

Length

2025-08-12T19:55:06.525373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-12T19:55:06.558416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
residential 51686
73.9%
condominium 9902
 
14.1%
commercial 3632
 
5.2%
lg 2157
 
3.1%
apartment 2157
 
3.1%
vacant 205
 
0.3%
land 205
 
0.3%
manufacturing 35
 
0.1%
exempt 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
i 126843
17.1%
e 109166
14.7%
n 74127
10.0%
d 61793
8.3%
a 58160
7.8%
t 56245
7.6%
l 55318
7.5%
R 51686
7.0%
s 51686
7.0%
m 29230
 
3.9%
Other values (15) 68158
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 742412
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 126843
17.1%
e 109166
14.7%
n 74127
10.0%
d 61793
8.3%
a 58160
7.8%
t 56245
7.6%
l 55318
7.5%
R 51686
7.0%
s 51686
7.0%
m 29230
 
3.9%
Other values (15) 68158
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 742412
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 126843
17.1%
e 109166
14.7%
n 74127
10.0%
d 61793
8.3%
a 58160
7.8%
t 56245
7.6%
l 55318
7.5%
R 51686
7.0%
s 51686
7.0%
m 29230
 
3.9%
Other values (15) 68158
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 742412
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 126843
17.1%
e 109166
14.7%
n 74127
10.0%
d 61793
8.3%
a 58160
7.8%
t 56245
7.6%
l 55318
7.5%
R 51686
7.0%
s 51686
7.0%
m 29230
 
3.9%
Other values (15) 68158
9.2%

Taxkey
Real number (ℝ)

High correlation  Missing 

Distinct25974
Distinct (%)86.9%
Missing37736
Missing (%)55.8%
Infinite0
Infinite (%)0.0%
Mean3.7344585 × 109
Minimum10021000
Maximum7.1699991 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.5 KiB
2025-08-12T19:55:06.605615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10021000
5-th percentile1.2199968 × 109
Q12.7060166 × 109
median3.591432 × 109
Q35.0708275 × 109
95-th percentile5.9401238 × 109
Maximum7.1699991 × 109
Range7.1599781 × 109
Interquartile range (IQR)2.364811 × 109

Descriptive statistics

Standard deviation1.479836 × 109
Coefficient of variation (CV)0.39626522
Kurtosis-0.66693275
Mean3.7344585 × 109
Median Absolute Deviation (MAD)1.111025 × 109
Skewness-0.089823652
Sum1.1165658 × 1014
Variance2.1899146 × 1018
MonotonicityNot monotonic
2025-08-12T19:55:06.658667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2620413000 5
 
< 0.1%
1750345200 5
 
< 0.1%
2801748000 4
 
< 0.1%
3601398000 4
 
< 0.1%
3922854000 4
 
< 0.1%
3551839000 4
 
< 0.1%
4090330000 4
 
< 0.1%
3020363000 4
 
< 0.1%
4290186000 4
 
< 0.1%
3591878000 4
 
< 0.1%
Other values (25964) 29857
44.1%
(Missing) 37736
55.8%
ValueCountFrequency (%)
10021000 1
< 0.1%
20041100 1
< 0.1%
30001100 2
< 0.1%
30031100 1
< 0.1%
30051000 1
< 0.1%
30131000 2
< 0.1%
30142000 1
< 0.1%
30183000 1
< 0.1%
30222000 1
< 0.1%
40061000 1
< 0.1%
ValueCountFrequency (%)
7169999120 1
 
< 0.1%
7169999110 1
 
< 0.1%
7169996114 3
< 0.1%
7169996111 1
 
< 0.1%
7160442000 1
 
< 0.1%
7160432000 1
 
< 0.1%
7160421000 1
 
< 0.1%
7160402100 1
 
< 0.1%
7160362000 1
 
< 0.1%
7160358000 2
< 0.1%
Distinct51309
Distinct (%)75.9%
Missing6
Missing (%)< 0.1%
Memory size528.5 KiB
2025-08-12T19:55:06.804853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length41
Median length37
Mean length16.499756
Min length12

Characters and Unicode

Total characters1115862
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40063 ?
Unique (%)59.2%

Sample

1st row7144 N 38TH ST
2nd row5346 N 52ND ST
3rd row4155 N 13TH ST
4th row3832 W HELENA ST
5th row4862 N 39TH ST
ValueCountFrequency (%)
st 42060
 
14.9%
n 30597
 
10.9%
av 17072
 
6.1%
w 16233
 
5.8%
s 16163
 
5.7%
e 4726
 
1.7%
unit 4243
 
1.5%
pl 2663
 
0.9%
dr 1788
 
0.6%
rd 1142
 
0.4%
Other values (13545) 145169
51.5%
2025-08-12T19:55:06.993457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
214402
19.2%
T 84123
 
7.5%
S 70594
 
6.3%
N 52722
 
4.7%
2 50916
 
4.6%
1 49638
 
4.4%
3 47543
 
4.3%
A 39844
 
3.6%
4 36120
 
3.2%
0 35886
 
3.2%
Other values (52) 434074
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1115862
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
214402
19.2%
T 84123
 
7.5%
S 70594
 
6.3%
N 52722
 
4.7%
2 50916
 
4.6%
1 49638
 
4.4%
3 47543
 
4.3%
A 39844
 
3.6%
4 36120
 
3.2%
0 35886
 
3.2%
Other values (52) 434074
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1115862
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
214402
19.2%
T 84123
 
7.5%
S 70594
 
6.3%
N 52722
 
4.7%
2 50916
 
4.6%
1 49638
 
4.4%
3 47543
 
4.3%
A 39844
 
3.6%
4 36120
 
3.2%
0 35886
 
3.2%
Other values (52) 434074
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1115862
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
214402
19.2%
T 84123
 
7.5%
S 70594
 
6.3%
N 52722
 
4.7%
2 50916
 
4.6%
1 49638
 
4.4%
3 47543
 
4.3%
A 39844
 
3.6%
4 36120
 
3.2%
0 35886
 
3.2%
Other values (52) 434074
38.9%

CondoProject
Text

Missing 

Distinct548
Distinct (%)5.1%
Missing56939
Missing (%)84.2%
Memory size528.5 KiB
2025-08-12T19:55:07.121864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length48
Median length34
Mean length17.181096
Min length5

Characters and Unicode

Total characters183769
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique183 ?
Unique (%)1.7%

Sample

1st row1522 ON THE LAKE CONDOMINIUMS
2nd row1522 ON THE LAKE CONDOMINIUMS
3rd row1522 ON THE LAKE CONDOMINIUMS
4th row1522 ON THE LAKE CONDOMINIUMS
5th row1522 ON THE LAKE CONDOMINIUMS
ValueCountFrequency (%)
condominium 1204
 
4.4%
on 1021
 
3.7%
lofts 981
 
3.6%
the 843
 
3.1%
lake 778
 
2.8%
2-br 659
 
2.4%
condos 651
 
2.4%
condominiums 618
 
2.3%
river 603
 
2.2%
1-br 417
 
1.5%
Other values (429) 19668
71.7%
2025-08-12T19:55:07.308680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18064
 
9.8%
O 16024
 
8.7%
E 15379
 
8.4%
R 13554
 
7.4%
N 11933
 
6.5%
I 11538
 
6.3%
A 10492
 
5.7%
T 9438
 
5.1%
L 8906
 
4.8%
S 8402
 
4.6%
Other values (51) 60039
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 183769
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18064
 
9.8%
O 16024
 
8.7%
E 15379
 
8.4%
R 13554
 
7.4%
N 11933
 
6.5%
I 11538
 
6.3%
A 10492
 
5.7%
T 9438
 
5.1%
L 8906
 
4.8%
S 8402
 
4.6%
Other values (51) 60039
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 183769
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18064
 
9.8%
O 16024
 
8.7%
E 15379
 
8.4%
R 13554
 
7.4%
N 11933
 
6.5%
I 11538
 
6.3%
A 10492
 
5.7%
T 9438
 
5.1%
L 8906
 
4.8%
S 8402
 
4.6%
Other values (51) 60039
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 183769
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18064
 
9.8%
O 16024
 
8.7%
E 15379
 
8.4%
R 13554
 
7.4%
N 11933
 
6.5%
I 11538
 
6.3%
A 10492
 
5.7%
T 9438
 
5.1%
L 8906
 
4.8%
S 8402
 
4.6%
Other values (51) 60039
32.7%

District
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7.957623
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.5 KiB
2025-08-12T19:55:07.342222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median8
Q311
95-th percentile14
Maximum15
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.2188374
Coefficient of variation (CV)0.53016301
Kurtosis-1.2792222
Mean7.957623
Median Absolute Deviation (MAD)3
Skewness-0.0066459046
Sum538182
Variance17.798589
MonotonicityNot monotonic
2025-08-12T19:55:07.373430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
5 7955
11.8%
11 6800
10.1%
14 5875
8.7%
10 5855
8.7%
13 5561
8.2%
3 5492
 
8.1%
9 4263
 
6.3%
2 4191
 
6.2%
4 4007
 
5.9%
7 3764
 
5.6%
Other values (5) 13868
20.5%
ValueCountFrequency (%)
1 3555
5.3%
2 4191
6.2%
3 5492
8.1%
4 4007
5.9%
5 7955
11.8%
6 3334
4.9%
7 3764
5.6%
8 2283
 
3.4%
9 4263
6.3%
10 5855
8.7%
ValueCountFrequency (%)
15 2246
 
3.3%
14 5875
8.7%
13 5561
8.2%
12 2450
 
3.6%
11 6800
10.1%
10 5855
8.7%
9 4263
6.3%
8 2283
 
3.4%
7 3764
5.6%
6 3334
4.9%

Nbhd
Real number (ℝ)

High correlation  Missing 

Distinct582
Distinct (%)1.9%
Missing37736
Missing (%)55.8%
Infinite0
Infinite (%)0.0%
Mean3822.75
Minimum40
Maximum6982
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.5 KiB
2025-08-12T19:55:07.412180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile780
Q12100
median4320
Q35283
95-th percentile6288
Maximum6982
Range6942
Interquartile range (IQR)3183

Descriptive statistics

Standard deviation1799.3994
Coefficient of variation (CV)0.47070812
Kurtosis-1.0733737
Mean3822.75
Median Absolute Deviation (MAD)1581
Skewness-0.23030669
Sum1.142964 × 108
Variance3237838.3
MonotonicityNot monotonic
2025-08-12T19:55:07.458692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2100 833
 
1.2%
2080 756
 
1.1%
4240 612
 
0.9%
4340 612
 
0.9%
4520 607
 
0.9%
4420 518
 
0.8%
4620 454
 
0.7%
4580 397
 
0.6%
2540 393
 
0.6%
4700 385
 
0.6%
Other values (572) 24332
36.0%
(Missing) 37736
55.8%
ValueCountFrequency (%)
40 67
 
0.1%
50 31
 
< 0.1%
240 271
0.4%
360 104
 
0.2%
380 52
 
0.1%
440 153
0.2%
480 293
0.4%
520 31
 
< 0.1%
560 142
0.2%
600 75
 
0.1%
ValueCountFrequency (%)
6982 3
 
< 0.1%
6981 9
 
< 0.1%
6980 5
 
< 0.1%
6979 9
 
< 0.1%
6978 12
 
< 0.1%
6977 15
 
< 0.1%
6976 22
< 0.1%
6975 7
 
< 0.1%
6974 38
0.1%
6973 4
 
< 0.1%

Style
Text

Distinct399
Distinct (%)0.6%
Missing103
Missing (%)0.2%
Memory size528.5 KiB
2025-08-12T19:55:07.600836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length50
Median length48
Mean length11.950335
Min length1

Characters and Unicode

Total characters807030
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique175 ?
Unique (%)0.3%

Sample

1st rowCape Cod
2nd rowCape Cod
3rd rowCape Cod
4th rowCape Cod
5th rowCape Cod
ValueCountFrequency (%)
ranch 14518
 
9.8%
o/s 10264
 
7.0%
cod 9721
 
6.6%
cape 9721
 
6.6%
bungalow 7461
 
5.1%
duplex 7427
 
5.0%
7387
 
5.0%
milwaukee 4226
 
2.9%
apartment 4053
 
2.7%
rise 3874
 
2.6%
Other values (294) 69009
46.7%
2025-08-12T19:55:07.814021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
81015
 
10.0%
e 61827
 
7.7%
a 46266
 
5.7%
o 46231
 
5.7%
n 41854
 
5.2%
l 36338
 
4.5%
i 31935
 
4.0%
R 28787
 
3.6%
C 27159
 
3.4%
t 26177
 
3.2%
Other values (63) 379441
47.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 807030
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
81015
 
10.0%
e 61827
 
7.7%
a 46266
 
5.7%
o 46231
 
5.7%
n 41854
 
5.2%
l 36338
 
4.5%
i 31935
 
4.0%
R 28787
 
3.6%
C 27159
 
3.4%
t 26177
 
3.2%
Other values (63) 379441
47.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 807030
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
81015
 
10.0%
e 61827
 
7.7%
a 46266
 
5.7%
o 46231
 
5.7%
n 41854
 
5.2%
l 36338
 
4.5%
i 31935
 
4.0%
R 28787
 
3.6%
C 27159
 
3.4%
t 26177
 
3.2%
Other values (63) 379441
47.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 807030
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
81015
 
10.0%
e 61827
 
7.7%
a 46266
 
5.7%
o 46231
 
5.7%
n 41854
 
5.2%
l 36338
 
4.5%
i 31935
 
4.0%
R 28787
 
3.6%
C 27159
 
3.4%
t 26177
 
3.2%
Other values (63) 379441
47.0%

Extwall
Categorical

High correlation  Missing 

Distinct22
Distinct (%)< 0.1%
Missing13643
Missing (%)20.2%
Memory size528.5 KiB
Aluminum/Vinyl
18208 
Brick
12176 
Aluminum / Vinyl
12127 
Frame
2505 
Stone
 
1751
Other values (17)
7225 

Length

Max length23
Median length17
Mean length11.271374
Min length4

Characters and Unicode

Total characters608564
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAluminum / Vinyl
2nd rowBrick
3rd rowBrick
4th rowAluminum / Vinyl
5th rowAluminum / Vinyl

Common Values

ValueCountFrequency (%)
Aluminum/Vinyl 18208
26.9%
Brick 12176
18.0%
Aluminum / Vinyl 12127
17.9%
Frame 2505
 
3.7%
Stone 1751
 
2.6%
Asphalt/Other 1700
 
2.5%
Wood 1434
 
2.1%
Stucco 874
 
1.3%
Masonry/Frame 768
 
1.1%
Masonry / Frame 656
 
1.0%
Other values (12) 1793
 
2.7%
(Missing) 13643
20.2%

Length

2025-08-12T19:55:07.871700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aluminum/vinyl 18208
22.5%
12783
15.8%
brick 12193
15.1%
aluminum 12127
15.0%
vinyl 12127
15.0%
frame 3317
 
4.1%
stone 1751
 
2.2%
asphalt/other 1700
 
2.1%
wood 1558
 
1.9%
stucco 874
 
1.1%
Other values (15) 4268
 
5.3%

Most occurring characters

ValueCountFrequency (%)
i 74133
12.2%
n 65710
10.8%
m 65537
10.8%
l 63902
10.5%
u 61790
10.2%
/ 33993
 
5.6%
y 32458
 
5.3%
A 32281
 
5.3%
V 30581
 
5.0%
26914
 
4.4%
Other values (23) 121265
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608564
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 74133
12.2%
n 65710
10.8%
m 65537
10.8%
l 63902
10.5%
u 61790
10.2%
/ 33993
 
5.6%
y 32458
 
5.3%
A 32281
 
5.3%
V 30581
 
5.0%
26914
 
4.4%
Other values (23) 121265
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608564
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 74133
12.2%
n 65710
10.8%
m 65537
10.8%
l 63902
10.5%
u 61790
10.2%
/ 33993
 
5.6%
y 32458
 
5.3%
A 32281
 
5.3%
V 30581
 
5.0%
26914
 
4.4%
Other values (23) 121265
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608564
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 74133
12.2%
n 65710
10.8%
m 65537
10.8%
l 63902
10.5%
u 61790
10.2%
/ 33993
 
5.6%
y 32458
 
5.3%
A 32281
 
5.3%
V 30581
 
5.0%
26914
 
4.4%
Other values (23) 121265
19.9%

Stories
Real number (ℝ)

High correlation  Zeros 

Distinct24
Distinct (%)< 0.1%
Missing189
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1.3569226
Minimum0
Maximum40
Zeros744
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size528.5 KiB
2025-08-12T19:55:07.909271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum40
Range40
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.61912585
Coefficient of variation (CV)0.45627205
Kurtosis393.12632
Mean1.3569226
Median Absolute Deviation (MAD)0
Skewness10.198618
Sum91519
Variance0.38331682
MonotonicityNot monotonic
2025-08-12T19:55:07.944735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 39343
58.2%
2 17458
25.8%
1.5 8695
 
12.9%
0 744
 
1.1%
3 609
 
0.9%
2.5 353
 
0.5%
4 101
 
0.1%
5 36
 
0.1%
8 21
 
< 0.1%
3.5 20
 
< 0.1%
Other values (14) 66
 
0.1%
(Missing) 189
 
0.3%
ValueCountFrequency (%)
0 744
 
1.1%
1 39343
58.2%
1.5 8695
 
12.9%
2 17458
25.8%
2.5 353
 
0.5%
3 609
 
0.9%
3.5 20
 
< 0.1%
4 101
 
0.1%
5 36
 
0.1%
6 19
 
< 0.1%
ValueCountFrequency (%)
40 1
 
< 0.1%
28 1
 
< 0.1%
26 1
 
< 0.1%
22 1
 
< 0.1%
19 1
 
< 0.1%
15 2
 
< 0.1%
14 10
< 0.1%
13 1
 
< 0.1%
12 5
< 0.1%
11 1
 
< 0.1%

Year_Built
Real number (ℝ)

Distinct182
Distinct (%)0.3%
Missing68
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1926.9291
Minimum0
Maximum2024
Zeros582
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size528.5 KiB
2025-08-12T19:55:07.985777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1892
Q11923
median1950
Q31959
95-th percentile2003
Maximum2024
Range2024
Interquartile range (IQR)36

Descriptive statistics

Standard deviation182.57968
Coefficient of variation (CV)0.094751632
Kurtosis104.36404
Mean1926.9291
Median Absolute Deviation (MAD)21
Skewness-10.170212
Sum1.3019682 × 108
Variance33335.34
MonotonicityNot monotonic
2025-08-12T19:55:08.031384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1955 2181
 
3.2%
1953 2063
 
3.1%
1952 1967
 
2.9%
1950 1856
 
2.7%
1956 1825
 
2.7%
1954 1722
 
2.5%
1951 1581
 
2.3%
1957 1561
 
2.3%
1958 1424
 
2.1%
1926 1339
 
2.0%
Other values (172) 50048
74.0%
ValueCountFrequency (%)
0 582
0.9%
203 2
 
< 0.1%
206 2
 
< 0.1%
910 1
 
< 0.1%
1835 1
 
< 0.1%
1836 2
 
< 0.1%
1840 1
 
< 0.1%
1843 1
 
< 0.1%
1844 1
 
< 0.1%
1848 1
 
< 0.1%
ValueCountFrequency (%)
2024 2
 
< 0.1%
2023 3
 
< 0.1%
2022 10
< 0.1%
2021 3
 
< 0.1%
2020 9
< 0.1%
2019 8
 
< 0.1%
2018 19
< 0.1%
2017 21
< 0.1%
2016 18
< 0.1%
2015 9
< 0.1%

Nr_of_rms
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct13
Distinct (%)< 0.1%
Missing37736
Missing (%)55.8%
Infinite0
Infinite (%)0.0%
Mean0.68356801
Minimum0
Maximum12
Zeros25173
Zeros (%)37.2%
Negative0
Negative (%)0.0%
Memory size528.5 KiB
2025-08-12T19:55:08.068268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6421153
Coefficient of variation (CV)2.4022706
Kurtosis3.5263383
Mean0.68356801
Median Absolute Deviation (MAD)0
Skewness2.1975571
Sum20438
Variance2.6965428
MonotonicityNot monotonic
2025-08-12T19:55:08.098461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 25173
37.2%
4 1875
 
2.8%
5 1337
 
2.0%
3 779
 
1.2%
6 443
 
0.7%
2 90
 
0.1%
7 82
 
0.1%
1 69
 
0.1%
8 38
 
0.1%
10 7
 
< 0.1%
Other values (3) 6
 
< 0.1%
(Missing) 37736
55.8%
ValueCountFrequency (%)
0 25173
37.2%
1 69
 
0.1%
2 90
 
0.1%
3 779
 
1.2%
4 1875
 
2.8%
5 1337
 
2.0%
6 443
 
0.7%
7 82
 
0.1%
8 38
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
11 2
 
< 0.1%
10 7
 
< 0.1%
9 3
 
< 0.1%
8 38
 
0.1%
7 82
 
0.1%
6 443
 
0.7%
5 1337
2.0%
4 1875
2.8%
3 779
1.2%

Fin_sqft
Real number (ℝ)

High correlation  Missing 

Distinct4170
Distinct (%)13.9%
Missing37736
Missing (%)55.8%
Infinite0
Infinite (%)0.0%
Mean2535.6908
Minimum0
Maximum378717
Zeros445
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size528.5 KiB
2025-08-12T19:55:08.135287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile763
Q11063
median1344
Q31943
95-th percentile4588.4
Maximum378717
Range378717
Interquartile range (IQR)880

Descriptive statistics

Standard deviation9259.7805
Coefficient of variation (CV)3.6517782
Kurtosis459.53887
Mean2535.6908
Median Absolute Deviation (MAD)356
Skewness18.340782
Sum75814620
Variance85743535
MonotonicityNot monotonic
2025-08-12T19:55:08.182370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 445
 
0.7%
1056 118
 
0.2%
980 117
 
0.2%
864 114
 
0.2%
936 109
 
0.2%
1120 96
 
0.1%
1054 96
 
0.1%
1200 92
 
0.1%
975 86
 
0.1%
1150 80
 
0.1%
Other values (4160) 28546
42.2%
(Missing) 37736
55.8%
ValueCountFrequency (%)
0 445
0.7%
140 1
 
< 0.1%
256 1
 
< 0.1%
320 1
 
< 0.1%
324 1
 
< 0.1%
325 25
 
< 0.1%
346 1
 
< 0.1%
405 3
 
< 0.1%
416 1
 
< 0.1%
430 4
 
< 0.1%
ValueCountFrequency (%)
378717 1
< 0.1%
360000 1
< 0.1%
330000 1
< 0.1%
280872 1
< 0.1%
280000 1
< 0.1%
240675 2
< 0.1%
230828 1
< 0.1%
230125 1
< 0.1%
218737 1
< 0.1%
210744 2
< 0.1%

Units
Real number (ℝ)

High correlation  Skewed 

Distinct102
Distinct (%)0.2%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.5479574
Minimum0
Maximum781
Zeros242
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size528.5 KiB
2025-08-12T19:55:08.227910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum781
Range781
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.2312891
Coefficient of variation (CV)5.3175167
Kurtosis5853.3962
Mean1.5479574
Median Absolute Deviation (MAD)0
Skewness69.803554
Sum104693
Variance67.754121
MonotonicityNot monotonic
2025-08-12T19:55:08.276976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 53200
78.7%
2 11536
 
17.1%
4 917
 
1.4%
3 828
 
1.2%
0 242
 
0.4%
8 175
 
0.3%
6 140
 
0.2%
5 115
 
0.2%
7 65
 
0.1%
12 52
 
0.1%
Other values (92) 363
 
0.5%
ValueCountFrequency (%)
0 242
 
0.4%
1 53200
78.7%
2 11536
 
17.1%
3 828
 
1.2%
4 917
 
1.4%
5 115
 
0.2%
6 140
 
0.2%
7 65
 
0.1%
8 175
 
0.3%
9 23
 
< 0.1%
ValueCountFrequency (%)
781 1
< 0.1%
737 1
< 0.1%
725 2
< 0.1%
720 1
< 0.1%
716 1
< 0.1%
431 1
< 0.1%
389 1
< 0.1%
303 1
< 0.1%
300 1
< 0.1%
191 1
< 0.1%

Bdrms
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct27
Distinct (%)< 0.1%
Missing1947
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean3.1396145
Minimum0
Maximum2031
Zeros3794
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size528.5 KiB
2025-08-12T19:55:08.319152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile6
Maximum2031
Range2031
Interquartile range (IQR)2

Descriptive statistics

Standard deviation8.0683938
Coefficient of variation (CV)2.5698676
Kurtosis60749.522
Mean3.1396145
Median Absolute Deviation (MAD)1
Skewness241.71703
Sum206235
Variance65.098978
MonotonicityNot monotonic
2025-08-12T19:55:08.763025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
3 25534
37.8%
4 12758
18.9%
2 12472
18.4%
0 3794
 
5.6%
6 3785
 
5.6%
5 3067
 
4.5%
1 3053
 
4.5%
8 461
 
0.7%
7 415
 
0.6%
9 108
 
0.2%
Other values (17) 241
 
0.4%
(Missing) 1947
 
2.9%
ValueCountFrequency (%)
0 3794
 
5.6%
1 3053
 
4.5%
2 12472
18.4%
3 25534
37.8%
4 12758
18.9%
5 3067
 
4.5%
6 3785
 
5.6%
7 415
 
0.6%
8 461
 
0.7%
9 108
 
0.2%
ValueCountFrequency (%)
2031 1
 
< 0.1%
43 1
 
< 0.1%
32 1
 
< 0.1%
29 1
 
< 0.1%
28 2
 
< 0.1%
25 2
 
< 0.1%
21 1
 
< 0.1%
20 4
< 0.1%
18 8
< 0.1%
17 1
 
< 0.1%

Fbath
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing657
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean1.3750187
Minimum0
Maximum10
Zeros5544
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size528.5 KiB
2025-08-12T19:55:08.798710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.71276177
Coefficient of variation (CV)0.51836516
Kurtosis1.2723518
Mean1.3750187
Median Absolute Deviation (MAD)0
Skewness0.3741544
Sum92096
Variance0.50802934
MonotonicityNot monotonic
2025-08-12T19:55:08.830585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 33881
50.1%
2 24849
36.7%
0 5544
 
8.2%
3 2372
 
3.5%
4 278
 
0.4%
5 42
 
0.1%
6 9
 
< 0.1%
10 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
(Missing) 657
 
1.0%
ValueCountFrequency (%)
0 5544
 
8.2%
1 33881
50.1%
2 24849
36.7%
3 2372
 
3.5%
4 278
 
0.4%
5 42
 
0.1%
6 9
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
6 9
 
< 0.1%
5 42
 
0.1%
4 278
 
0.4%
3 2372
 
3.5%
2 24849
36.7%
1 33881
50.1%
0 5544
 
8.2%

Hbath
Real number (ℝ)

Missing  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing4842
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean0.31710541
Minimum0
Maximum10
Zeros44282
Zeros (%)65.5%
Negative0
Negative (%)0.0%
Memory size528.5 KiB
2025-08-12T19:55:08.859940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.51385129
Coefficient of variation (CV)1.6204431
Kurtosis2.9673776
Mean0.31710541
Median Absolute Deviation (MAD)0
Skewness1.4241936
Sum19912
Variance0.26404314
MonotonicityNot monotonic
2025-08-12T19:55:08.888063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 44282
65.5%
1 17171
 
25.4%
2 1287
 
1.9%
3 51
 
0.1%
10 1
 
< 0.1%
4 1
 
< 0.1%
(Missing) 4842
 
7.2%
ValueCountFrequency (%)
0 44282
65.5%
1 17171
 
25.4%
2 1287
 
1.9%
3 51
 
0.1%
4 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
4 1
 
< 0.1%
3 51
 
0.1%
2 1287
 
1.9%
1 17171
 
25.4%
0 44282
65.5%

Lotsize
Unsupported

Rejected  Unsupported 

Missing3
Missing (%)< 0.1%
Memory size528.5 KiB
Distinct2001
Distinct (%)3.0%
Missing2
Missing (%)< 0.1%
Memory size528.5 KiB
Minimum2004-01-01 00:00:00
Maximum2028-06-13 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-08-12T19:55:08.924894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:08.972572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Sale_price
Unsupported

Rejected  Unsupported 

Missing12
Missing (%)< 0.1%
Memory size528.5 KiB

PropertyID
Real number (ℝ)

Missing 

Distinct35162
Distinct (%)93.2%
Missing29901
Missing (%)44.2%
Infinite0
Infinite (%)0.0%
Mean412311.08
Minimum98422
Maximum881476
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.5 KiB
2025-08-12T19:55:09.020054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum98422
5-th percentile110093.85
Q1158895.25
median222696
Q3856787.75
95-th percentile875350.35
Maximum881476
Range783054
Interquartile range (IQR)697892.5

Descriptive statistics

Standard deviation327264.27
Coefficient of variation (CV)0.79373146
Kurtosis-1.5442108
Mean412311.08
Median Absolute Deviation (MAD)86564
Skewness0.62829354
Sum1.5558146 × 1010
Variance1.071019 × 1011
MonotonicityNot monotonic
2025-08-12T19:55:09.067131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
878421 16
 
< 0.1%
215821 8
 
< 0.1%
200812 6
 
< 0.1%
137976 4
 
< 0.1%
119688 4
 
< 0.1%
211474 4
 
< 0.1%
137389 4
 
< 0.1%
203833 4
 
< 0.1%
155033 4
 
< 0.1%
157521 4
 
< 0.1%
Other values (35152) 37676
55.7%
(Missing) 29901
44.2%
ValueCountFrequency (%)
98422 1
< 0.1%
98423 1
< 0.1%
98453 1
< 0.1%
98459 1
< 0.1%
98461 1
< 0.1%
98464 1
< 0.1%
98477 1
< 0.1%
98490 1
< 0.1%
98495 1
< 0.1%
98500 1
< 0.1%
ValueCountFrequency (%)
881476 1
< 0.1%
879934 1
< 0.1%
879928 1
< 0.1%
879890 1
< 0.1%
879889 1
< 0.1%
879886 1
< 0.1%
879884 1
< 0.1%
879874 1
< 0.1%
879871 1
< 0.1%
879853 1
< 0.1%

taxkey
Real number (ℝ)

High correlation  Missing 

Distinct32664
Distinct (%)86.6%
Missing29901
Missing (%)44.2%
Infinite0
Infinite (%)0.0%
Mean3.4888337 × 109
Minimum10011000
Maximum7.16038 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.5 KiB
2025-08-12T19:55:09.112605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10011000
5-th percentile1.1701378 × 109
Q12.4520538 × 109
median3.2204656 × 109
Q34.90119 × 109
95-th percentile5.8104024 × 109
Maximum7.16038 × 109
Range7.150369 × 109
Interquartile range (IQR)2.4491362 × 109

Descriptive statistics

Standard deviation1.4922663 × 109
Coefficient of variation (CV)0.42772641
Kurtosis-0.70632985
Mean3.4888337 × 109
Median Absolute Deviation (MAD)1.089949 × 109
Skewness0.13154817
Sum1.3164765 × 1014
Variance2.2268588 × 1018
MonotonicityNot monotonic
2025-08-12T19:55:09.158090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5360559000 16
 
< 0.1%
4611469100 8
 
< 0.1%
3970173100 6
 
< 0.1%
2860938000 5
 
< 0.1%
1180100000 5
 
< 0.1%
4971235000 4
 
< 0.1%
341146000 4
 
< 0.1%
2530623000 4
 
< 0.1%
4351044000 4
 
< 0.1%
3190972000 4
 
< 0.1%
Other values (32654) 37674
55.7%
(Missing) 29901
44.2%
ValueCountFrequency (%)
10011000 1
< 0.1%
10021000 1
< 0.1%
30023110 1
< 0.1%
30091000 1
< 0.1%
30131000 1
< 0.1%
30152000 1
< 0.1%
30171000 1
< 0.1%
39995000 1
< 0.1%
40012000 1
< 0.1%
40061000 1
< 0.1%
ValueCountFrequency (%)
7160380000 2
< 0.1%
7160379000 1
< 0.1%
7160375000 1
< 0.1%
7160373000 1
< 0.1%
7160372000 1
< 0.1%
7160369000 1
< 0.1%
7160367000 1
< 0.1%
7160366000 2
< 0.1%
7160365000 2
< 0.1%
7160363000 2
< 0.1%

nbhd
Real number (ℝ)

High correlation  Missing 

Distinct602
Distinct (%)1.6%
Missing29958
Missing (%)44.3%
Infinite0
Infinite (%)0.0%
Mean3373.6933
Minimum40
Maximum42703
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.5 KiB
2025-08-12T19:55:09.203327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile700
Q11840
median3060
Q34660
95-th percentile6277
Maximum42703
Range42663
Interquartile range (IQR)2820

Descriptive statistics

Standard deviation1819.2295
Coefficient of variation (CV)0.53923973
Kurtosis14.409915
Mean3373.6933
Median Absolute Deviation (MAD)1520
Skewness0.90967125
Sum1.2711064 × 108
Variance3309595.9
MonotonicityNot monotonic
2025-08-12T19:55:09.246181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2100 852
 
1.3%
4520 667
 
1.0%
2080 659
 
1.0%
4420 621
 
0.9%
1140 614
 
0.9%
4340 567
 
0.8%
4240 566
 
0.8%
4120 545
 
0.8%
4620 481
 
0.7%
480 436
 
0.6%
Other values (592) 31669
46.8%
(Missing) 29958
44.3%
ValueCountFrequency (%)
40 99
 
0.1%
50 43
 
0.1%
240 323
0.5%
360 156
 
0.2%
380 68
 
0.1%
440 203
0.3%
480 436
0.6%
520 41
 
0.1%
560 202
0.3%
600 152
 
0.2%
ValueCountFrequency (%)
42703 1
 
< 0.1%
38705 2
 
< 0.1%
32327 1
 
< 0.1%
24910 1
 
< 0.1%
18107 1
 
< 0.1%
6987 1
 
< 0.1%
6986 2
 
< 0.1%
6982 2
 
< 0.1%
6981 2
 
< 0.1%
6980 6
< 0.1%

Rooms
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct42
Distinct (%)0.1%
Missing31847
Missing (%)47.1%
Infinite0
Infinite (%)0.0%
Mean6.7300771
Minimum0
Maximum70
Zeros935
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size528.5 KiB
2025-08-12T19:55:09.284464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q15
median6
Q38
95-th percentile12
Maximum70
Range70
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2426506
Coefficient of variation (CV)0.48181478
Kurtosis17.232176
Mean6.7300771
Median Absolute Deviation (MAD)1
Skewness2.1868211
Sum240856
Variance10.514783
MonotonicityNot monotonic
2025-08-12T19:55:09.326287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
5 9247
 
13.7%
6 6317
 
9.3%
4 4178
 
6.2%
10 3610
 
5.3%
7 3102
 
4.6%
8 2583
 
3.8%
9 1535
 
2.3%
12 1338
 
2.0%
0 935
 
1.4%
3 791
 
1.2%
Other values (32) 2152
 
3.2%
(Missing) 31847
47.1%
ValueCountFrequency (%)
0 935
 
1.4%
1 57
 
0.1%
2 100
 
0.1%
3 791
 
1.2%
4 4178
6.2%
5 9247
13.7%
6 6317
9.3%
7 3102
 
4.6%
8 2583
 
3.8%
9 1535
 
2.3%
ValueCountFrequency (%)
70 1
 
< 0.1%
63 1
 
< 0.1%
62 1
 
< 0.1%
45 2
< 0.1%
44 1
 
< 0.1%
40 2
< 0.1%
39 1
 
< 0.1%
38 2
< 0.1%
36 4
< 0.1%
33 1
 
< 0.1%

FinishedSqft
Unsupported

Missing  Rejected  Unsupported 

Missing30013
Missing (%)44.4%
Memory size528.5 KiB

Interactions

2025-08-12T19:55:05.263231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:56.685938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.295444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.838471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.336725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.895456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.473858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.962364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.535895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:01.113748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.262920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.856729image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.447290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.022972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.628399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:05.296287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:56.747689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.331661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.875956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.372143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.935173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.509640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.005218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.573160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:01.157302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.302327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.894926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.476241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.050088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.654929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:05.338089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:56.805940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.366958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.912060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.409505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.971316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.543104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.073743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.610241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:01.201617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.337878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.931660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.518329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.087232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.693561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:05.373010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:56.860595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.404391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.949792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.446072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.011206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.578436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.117816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.646365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:01.244519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.378200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.973693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.545495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.112487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.721301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:05.427892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:56.918325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.441670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.984669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.485476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.051293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.612887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.154398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.688580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:01.283260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.419401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.015410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.589039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.158817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.762618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:05.470892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:56.979507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.478203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.023077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.524581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.090353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.649255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.197685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.727613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:01.324755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.458975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.054578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.634341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.199312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.802126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:05.499267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.014770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.513829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.059908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.558971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.126682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.683763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.240874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.761912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:01.381275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.493552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.092236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.659694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.226397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.827956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:05.528214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.053533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.550803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.097214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.596249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.166721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.719704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.281159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.799472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:01.424660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.531538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.136641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.687098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.254281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.857204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:05.569231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.088080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.588921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.133748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.636360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.208063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.753669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.319920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.842781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:01.470181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.574290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.178491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.731047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.358636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.894843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:05.611469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.125586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.627006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.171083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.673520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.248107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.790241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.359219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.880744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:01.511425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.613767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.220214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.772120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.397735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.934684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:05.651464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.160065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.662595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.206761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.710969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.285075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.823956image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.399595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.919587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:01.548345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.648923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.256646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.814310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.436214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.979886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:05.688914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.195817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.695644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.241246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.746150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.320741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.857416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.439234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.954423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:01.880670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.686494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.291731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.854615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.474206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:05.019998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:05.729499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.223033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.734633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.266151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.786950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.363581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.882571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.464269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.995133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:01.916613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.737093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.334137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.896756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.514780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:05.083945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:05.769613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.246753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.769528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.289428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.824111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.400779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.906422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.487544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:01.033573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.094400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.777058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.370970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.940878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.554344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:05.176376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:05.808854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.270117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:57.803286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.313283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:58.859504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.437099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:54:59.934269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:00.511520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:01.073610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.196759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:02.818672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.408526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:03.981123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:04.590048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T19:55:05.216219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-12T19:55:09.366558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
BdrmsDistrictExtwallFbathFin_sqftHbathNbhdNr_of_rmsPropTypePropertyIDRoomsStoriesTaxkeyUnitsYear_Builtnbhdtaxkey
Bdrms1.0000.1030.0000.5040.2320.118-0.590-0.3440.000-0.0450.8430.350-0.0030.504-0.228-0.294-0.014
District0.1031.0000.109-0.009-0.006-0.0020.218-0.2330.1750.2910.1190.0450.6900.049-0.1280.3490.665
Extwall0.0000.1091.0000.0671.0000.0570.1311.0000.3720.0800.0510.1780.0930.1560.0290.1880.103
Fbath0.504-0.0090.0671.0000.185-0.064-0.2800.1530.1020.0180.5290.2630.0180.389-0.074-0.1180.045
Fin_sqft0.232-0.0061.0000.1851.0000.0630.134-0.1930.113NaNNaN0.606-0.0350.390-0.123NaNNaN
Hbath0.118-0.0020.057-0.0640.0631.000-0.1880.0060.029-0.075-0.0040.123-0.058-0.1770.230-0.094-0.090
Nbhd-0.5900.2180.131-0.2800.134-0.1881.0000.4510.648NaNNaN0.0660.480-0.1600.071NaNNaN
Nr_of_rms-0.344-0.2331.0000.153-0.1930.0060.4511.0000.482NaNNaN-0.043-0.045-0.1620.345NaNNaN
PropType0.0000.1750.3720.1020.1130.0290.6480.4821.0000.0670.1110.0590.2530.0470.3420.4520.206
PropertyID-0.0450.2910.0800.018NaN-0.075NaNNaN0.0671.000-0.025-0.016NaN-0.006-0.0850.2540.440
Rooms0.8430.1190.0510.529NaN-0.004NaNNaN0.111-0.0251.0000.484NaN0.530-0.361-0.2460.017
Stories0.3500.0450.1780.2630.6060.1230.066-0.0430.059-0.0160.4841.000-0.0120.544-0.1810.1230.015
Taxkey-0.0030.6900.0930.018-0.035-0.0580.480-0.0450.253NaNNaN-0.0121.0000.023-0.111NaNNaN
Units0.5040.0490.1560.3890.390-0.177-0.160-0.1620.047-0.0060.5300.5440.0231.000-0.2390.1230.033
Year_Built-0.228-0.1280.029-0.074-0.1230.2300.0710.3450.342-0.085-0.361-0.181-0.111-0.2391.0000.032-0.199
nbhd-0.2940.3490.188-0.118NaN-0.094NaNNaN0.4520.254-0.2460.123NaN0.1230.0321.0000.613
taxkey-0.0140.6650.1030.045NaN-0.090NaNNaN0.2060.4400.0170.015NaN0.033-0.1990.6131.000

Missing values

2025-08-12T19:55:05.888561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-12T19:55:06.031073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-08-12T19:55:06.348363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PropTypeTaxkeyAddressCondoProjectDistrictNbhdStyleExtwallStoriesYear_BuiltNr_of_rmsFin_sqftUnitsBdrmsFbathHbathLotsizeSale_dateSale_pricePropertyIDtaxkeynbhdRoomsFinishedSqft
0Residential1.230322e+097144 N 38TH STNaN1.0600.0Cape CodAluminum / Vinyl1.51959.00.01734.01.04.02.00.070002018-07129000NaNNaNNaNNaNNaN
1Residential1.909905e+095346 N 52ND STNaN1.01140.0Cape CodBrick1.01951.00.01718.01.03.02.01.081002018-0867000NaNNaNNaNNaNNaN
2Residential2.441200e+094155 N 13TH STNaN1.01620.0Cape CodBrick1.51938.00.01614.01.03.01.01.050402018-02103000NaNNaNNaNNaNNaN
3Residential1.230292e+093832 W HELENA STNaN1.0600.0Cape CodAluminum / Vinyl1.51960.00.01582.01.04.02.00.060002018-03150000NaNNaNNaNNaNNaN
4Residential2.080590e+094862 N 39TH STNaN1.01440.0Cape CodAluminum / Vinyl1.01954.00.01556.01.04.02.01.049202018-0389900NaNNaNNaNNaNNaN
5Residential2.060683e+094839 N 24TH PLNaN1.01340.0Cape CodAluminum / Vinyl1.01949.00.01455.01.03.01.00.050802018-0783000NaNNaNNaNNaNNaN
6Residential2.320316e+094645 N GREEN BAY AVNaN1.01340.0Cape CodStone1.01948.00.01437.01.03.01.01.088202018-11104000NaNNaNNaNNaNNaN
7Residential1.960126e+095517 N 11TH STNaN1.01220.0Cape CodBrick1.01951.00.01413.01.03.01.00.067502018-07146200NaNNaNNaNNaNNaN
8Residential1.719596e+095964 N 39TH STNaN1.0900.0Cape CodAluminum / Vinyl1.01949.00.01411.01.04.01.01.058882018-0570000NaNNaNNaNNaNNaN
9Residential2.061630e+094880 N 21ST STNaN1.01340.0Cape CodFrame1.51951.00.01409.01.05.02.00.048302018-0589000NaNNaNNaNNaNNaN
PropTypeTaxkeyAddressCondoProjectDistrictNbhdStyleExtwallStoriesYear_BuiltNr_of_rmsFin_sqftUnitsBdrmsFbathHbathLotsizeSale_dateSale_pricePropertyIDtaxkeynbhdRoomsFinishedSqft
67625Commercial3.051129e+096404 W LISBON AVNaN10.06226.0LaundromatNaN1.01940.00.02198.01.00.00.00.070982011-12105000NaNNaNNaNNaNNaN
67626Commercial5.100497e+092901 W KINNICKINNIC RIVER PKNaN11.06441.0Medical ClinicNaN1.01982.00.07921.01.00.00.00.021842011-121349275NaNNaNNaNNaNNaN
67627Commercial5.100484e+092901 W KINNICKINNIC RIVER PKNaN11.06441.0Medical ClinicNaN1.01982.00.03628.01.00.00.00.09882011-12604014NaNNaNNaNNaNNaN
67628Commercial3.201540e+088120 W BROWN DEER RDNaN9.06423.0Mini WarehouseNaN1.02004.00.061667.01.00.00.00.03056172011-125075000NaNNaNNaNNaNNaN
67629Commercial4.000604e+092017 W WISCONSIN AVNaN4.06268.0Residence With Commercial UsageNaN2.51913.00.07703.01.00.00.00.0150002011-12650000NaNNaNNaNNaNNaN
67630Commercial2.080600e+094825 N HOPKINS STNaN1.06232.0Store Bldg - Multi Story (Store & Apt, Store & ONaN2.01930.00.01970.01.00.00.00.051302011-1230000NaNNaNNaNNaNNaN
67631Commercial4.304220e+088301 W BROWN DEER RDNaN9.06202.0Store Building - Single tenant, 1 storyNaN1.01980.00.01703.01.00.00.00.0187532011-12650000NaNNaNNaNNaNNaN
67632Commercial2.821613e+093249 N 3RD STNaN6.06240.0Store Building - Single tenant, 1 storyNaN1.01895.00.01144.01.00.00.00.047502011-1240972NaNNaNNaNNaNNaN
67633Commercial4.611344e+091665 S 11TH STNaN12.06280.0TavernNaN2.01898.00.03420.01.00.00.00.042002011-1270000NaNNaNNaNNaNNaN
67634Commercial3.018300e+0710512 W GLENBROOK CTNaN9.06202.0Warehouse Building - 1 StoryNaN1.01976.00.06400.01.00.00.00.0864232011-12195000NaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

PropTypeTaxkeyAddressCondoProjectDistrictNbhdStyleExtwallStoriesYear_BuiltNr_of_rmsFin_sqftUnitsBdrmsFbathHbathSale_datePropertyIDtaxkeynbhdRooms# duplicates
69Lg ApartmentNaN225-233 W WISCONSIN AVNaN4.0NaNSubsidized ApartmentsPrecast Masonary14.01907.0NaNNaN136.01.0NaNNaN10/30/2019200812.03.970173e+096974.00.04
77Lg ApartmentNaN710 W HISTORIC MITCHELL STNaN12.0NaNSubsidized ApartmentsBrick8.01910.0NaNNaN68.01.0NaNNaN2/15/2019215821.04.611469e+096978.00.04
78Lg ApartmentNaN710 W HISTORIC MITCHELL STNaN12.0NaNSubsidized ApartmentsBrick8.01910.0NaNNaN68.02.0NaNNaN2/15/2019215821.04.611469e+096978.00.04
97Residential1.750345e+095853 N 74TH STNaN2.0980.0RanchAluminum / Vinyl1.01952.00.0969.01.02.01.00.02009-05NaNNaNNaNNaN4
32CommercialNaN3737-3739 W NATIONAL AVNaN8.0NaNStore Bldg - Multi Story (Store & Apt, Store & OfcBrick2.01930.0NaNNaN6.01.0NaNNaN1/31/2019211944.04.360312e+096274.00.03
66Lg ApartmentNaN1855 N CAMBRIDGE AVNaN3.0NaNAP4 (Conv Apt with 21 or more Units)Brick4.01963.0NaNNaN30.01.0NaNNaN11/22/2019189598.03.550037e+096974.00.03
67Lg ApartmentNaN1857 E KENILWORTH PLNaN3.0NaNAP4 (Conv Apt with 21 or more Units)Precast Masonary8.02009.0NaNNaN95.01.0NaNNaN3/14/2019190881.03.561521e+096972.00.03
144Residential4.700503e+092119 W BURNHAM STNaN8.04120.0Residence O/SFrame1.01896.00.01328.01.04.01.00.02013-05NaNNaNNaNNaN3
182Vacant Land6.900403e+096020 S 27TH STNaN13.04860.0**NaN0.00.00.00.00.00.00.00.02009-03NaNNaNNaNNaN3
0Commercial1.569984e+095135 W MILL RDNaN9.06216.0Parking LotNaN0.00.00.00.01.00.00.00.02010-12NaNNaNNaNNaN2